There is a growing interest in estimating heterogeneous treatment effects
across individuals using their high-dimensional feature attributes. Achieving
high performance in such high-dimensional heterogeneous treatment effect
estimation is challenging because in this setup, it is usual that some features
induce sample selection bias while others do not but are predictive of
potential outcomes. To avoid losing such predictive feature information,
existing methods learn separate feature representations using the inverse of
probability weighting (IPW). However, due to the numerically unstable IPW
weights, they suffer from estimation bias under a finite sample setup. To
develop a numerically robust estimator via weighted representation learning, we
propose a differentiable Pareto-smoothed weighting framework that replaces
extreme weight values in an end-to-end fashion. Experimental results show that
by effectively correcting the weight values, our method outperforms the
existing ones, including traditional weighting schemes.

我们提出了一种可通过不同 iable Pareto 平滑加权框架来替换极端权重值的可微分权重学习方法，从而在估算高维异质处理效应时实现数值稳健的估计，实验结果表明，通过有效地校正权重值，我们的方法优于现有方法，包括传统的加权方案。